Using the regularization functions defined above, we can develop diversity-regularized variants of the the MaxminDQN and EnsembleDQN algorithms. The training technique is identical to the algorithms described in [1]} and [2]}, with a regularization term added to the loss of the Q-functions. The loss term for \(i\) -th Q-function with parameters \(\psi _i\)  is:
\(\mathcal {L}\left(\psi _i\right)=\mathbb {E}_{s,a,r, s^{\prime }}\left[\left( Q_{\psi }^{i}\left(s,a\right)-Y\right)^2 \right] - \lambda \mathcal {I}\left(\ell _i, \ell \right),\) 
